Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/crates/ruvector-mincut/benches/README.md

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+# RuVector MinCut Benchmarks
+
+Comprehensive benchmark suite for the dynamic minimum cut algorithm implementation.
+
+## Overview
+
+This benchmark suite measures the performance characteristics of the dynamic graph operations that underpin the minimum cut algorithm, including:
+
+- **Edge Operations**: Insert/delete throughput and scaling
+- **Query Performance**: Connectivity, degree, edge existence checks
+- **Workload Patterns**: Mixed operations, batch processing
+- **Scaling Analysis**: Verification of subpolynomial update time (O(n^(2/3)))
+- **Graph Types**: Random, grid, complete, sparse, and dense graphs
+
+## Running Benchmarks
+
+### Run All Benchmarks
+
+```bash
+cd crates/ruvector-mincut
+cargo bench --bench mincut_bench
+```
+
+### Run Specific Benchmark Groups
+
+```bash
+# Graph operations (insert, delete, queries)
+cargo bench --bench mincut_bench graph_ops
+
+# Query operations
+cargo bench --bench mincut_bench queries
+
+# Workload patterns
+cargo bench --bench mincut_bench workloads
+
+# Scaling analysis
+cargo bench --bench mincut_bench scaling
+```
+
+### Run Individual Benchmarks
+
+```bash
+# Insert edge performance
+cargo bench --bench mincut_bench -- insert_edge
+
+# Delete edge performance
+cargo bench --bench mincut_bench -- delete_edge
+
+# Mixed workload
+cargo bench --bench mincut_bench -- mixed_workload
+
+# Scaling analysis
+cargo bench --bench mincut_bench -- scaling_analysis
+```
+
+### Filter by Size
+
+```bash
+# Benchmark only size 1000
+cargo bench --bench mincut_bench -- /1000
+
+# Benchmark sizes 100 and 5000
+cargo bench --bench mincut_bench -- "/100|5000"
+```
+
+## Benchmark Groups
+
+### 1. Graph Operations (`graph_ops`)
+
+- **`insert_edge`**: Edge insertion throughput at various graph sizes (100 to 10K vertices)
+- **`delete_edge`**: Edge deletion throughput
+- **`degree_query`**: Vertex degree query performance
+- **`has_edge_query`**: Edge existence check performance
+
+### 2. Query Operations (`queries`)
+
+- **`query_connectivity`**: Graph connectivity checking (BFS-based)
+- **`stats_computation`**: Graph statistics calculation
+- **`connected_components`**: Connected components computation
+- **`neighbors_iteration`**: Neighbor list retrieval
+
+### 3. Workload Patterns (`workloads`)
+
+- **`mixed_workload`**: Realistic mixed operations (50% insert, 30% delete, 20% query)
+- **`batch_operations`**: Batch insertion performance (10 to 1000 edges)
+
+### 4. Scaling Analysis (`scaling`)
+
+- **`scaling_analysis`**: Verify subpolynomial O(n^(2/3)) update time
+  - Tests sizes: 100, 316, 1K, 3.2K, 10K vertices
+  - Both insert and delete operations
+- **`graph_types`**: Performance across different graph structures
+  - Random graphs
+  - Grid graphs (structured)
+  - Complete graphs (dense)
+  - Sparse graphs (avg degree ~4)
+  - Dense graphs (~30% edge density)
+- **`memory_efficiency`**: Graph creation and memory footprint
+
+## Graph Generators
+
+The benchmark suite includes several graph generators:
+
+- **Random Graph**: `generate_random_graph(n, m, seed)` - n vertices, m random edges
+- **Grid Graph**: `generate_grid_graph(width, height)` - Structured 2D grid
+- **Complete Graph**: `generate_complete_graph(n)` - All possible edges
+- **Sparse Graph**: `generate_sparse_graph(n, seed)` - Average degree ~4
+- **Dense Graph**: `generate_dense_graph(n, seed)` - ~30% edge density
+
+## Expected Performance Characteristics
+
+Based on the theoretical analysis:
+
+### Update Operations (Insert/Delete)
+- **Time Complexity**: O(n^(2/3)) amortized
+- **Scaling**: Should show subpolynomial growth
+- **Throughput**: Higher for smaller graphs
+
+### Query Operations
+- **Connectivity**: O(n + m) via BFS
+- **Degree**: O(1) via adjacency list lookup
+- **Has Edge**: O(1) via hash table lookup
+- **Connected Components**: O(n + m) via BFS
+
+### Graph Types
+- **Sparse graphs**: Better cache locality, faster updates
+- **Dense graphs**: More edges to maintain, slower updates
+- **Grid graphs**: Good locality, predictable performance
+- **Complete graphs**: Maximum edges, highest overhead
+
+## Interpreting Results
+
+### Scaling Verification
+
+The `scaling_analysis` benchmarks test sizes: 100, 316, 1000, 3162, 10000
+
+For O(n^(2/3)) complexity:
+- 100 → 316 (3.16x): expect ~2.1x slowdown
+- 316 → 1000 (3.16x): expect ~2.1x slowdown
+- 1000 → 3162 (3.16x): expect ~2.1x slowdown
+- 3162 → 10000 (3.16x): expect ~2.1x slowdown
+
+If you observe linear or worse scaling, there may be algorithmic issues.
+
+### Throughput Metrics
+
+Criterion reports:
+- **Time/iteration**: Lower is better
+- **Throughput**: Higher is better
+- **R²**: Close to 1.0 indicates stable measurements
+
+### Performance Baseline
+
+Typical results on modern hardware (approximate):
+- Small graphs (100-500): ~100-500 ns/operation
+- Medium graphs (1K-5K): ~500ns-2μs/operation
+- Large graphs (10K+): ~2-10μs/operation
+
+## Customizing Benchmarks
+
+### Adjust Sample Sizes
+
+Modify `group.sample_size()` in the benchmark code:
+
+```rust
+let mut group = c.benchmark_group("my_group");
+group.sample_size(50); // Default is 100
+```
+
+### Add Custom Graph Sizes
+
+Edit the size arrays in each benchmark:
+
+```rust
+for size in [100, 500, 1000, 2500, 5000, 10000].iter() {
+    // ...
+}
+```
+
+### Change Workload Mix
+
+Modify the `bench_mixed_workload` ratios:
+
+```rust
+match op {
+    0..=5 => { /* 60% inserts */ },
+    6..=8 => { /* 30% deletes */ },
+    _ => { /* 10% queries */ }
+}
+```
+
+## Output
+
+Benchmark results are saved to:
+- `target/criterion/`: HTML reports with graphs
+- `target/criterion/{benchmark_name}/report/index.html`: Detailed reports
+
+Open in browser:
+```bash
+open target/criterion/report/index.html
+```
+
+## Continuous Integration
+
+Add to CI pipeline:
+
+```yaml
+- name: Run benchmarks
+  run: |
+    cd crates/ruvector-mincut
+    cargo bench --bench mincut_bench -- --output-format bencher | tee bench_output.txt
+```
+
+Compare against baseline:
+```bash
+cargo install critcmp
+critcmp baseline current
+```
+
+## Troubleshooting
+
+### Benchmarks Take Too Long
+
+Reduce sample size or iterations:
+```rust
+group.sample_size(10);
+group.measurement_time(Duration::from_secs(5));
+```
+
+### Unstable Results
+
+- Close other applications
+- Disable CPU frequency scaling
+- Run with `nice -n -20` for higher priority
+- Check system load: `uptime`
+
+### Out of Memory
+
+Reduce graph sizes:
+```rust
+for size in [100, 500, 1000].iter() { // Skip 5K and 10K
+```
+
+## References
+
+- [Criterion.rs Documentation](https://bheisler.github.io/criterion.rs/book/)
+- Dynamic Min-Cut Algorithm: O(n^(2/3)) update time
+- Graph Connectivity: BFS in O(n + m)

vendor/ruvector/crates/ruvector-mincut/benches/bounded_bench.rs

@@ -0,0 +1,496 @@
+//! Benchmarks for bounded-range dynamic minimum cut
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use rand::rngs::StdRng;
+use rand::{Rng, SeedableRng};
+use ruvector_mincut::prelude::*;
+use ruvector_mincut::wrapper::MinCutWrapper;
+use std::sync::Arc;
+
+/// Generate a random graph with n vertices and m edges
+fn generate_random_edges(n: usize, m: usize, seed: u64) -> Vec<(u64, u64)> {
+    let mut rng = StdRng::seed_from_u64(seed);
+    let mut edges = Vec::with_capacity(m);
+    let mut seen = std::collections::HashSet::new();
+
+    while edges.len() < m {
+        let u = rng.gen_range(0..n as u64);
+        let v = rng.gen_range(0..n as u64);
+        if u != v {
+            let key = if u < v { (u, v) } else { (v, u) };
+            if seen.insert(key) {
+                edges.push((u, v));
+            }
+        }
+    }
+
+    edges
+}
+
+/// Generate path graph edges
+fn generate_path_edges(n: usize) -> Vec<(u64, u64)> {
+    (0..n as u64 - 1).map(|i| (i, i + 1)).collect()
+}
+
+/// Generate cycle graph edges
+fn generate_cycle_edges(n: usize) -> Vec<(u64, u64)> {
+    let mut edges: Vec<_> = (0..n as u64 - 1).map(|i| (i, i + 1)).collect();
+    edges.push((n as u64 - 1, 0));
+    edges
+}
+
+/// Generate complete graph edges
+fn generate_complete_edges(n: usize) -> Vec<(u64, u64)> {
+    let mut edges = Vec::new();
+    for i in 0..n as u64 {
+        for j in (i + 1)..n as u64 {
+            edges.push((i, j));
+        }
+    }
+    edges
+}
+
+/// Generate grid graph edges
+fn generate_grid_edges(width: usize, height: usize) -> Vec<(u64, u64)> {
+    let mut edges = Vec::new();
+    for i in 0..height {
+        for j in 0..width {
+            let v = (i * width + j) as u64;
+            if j + 1 < width {
+                edges.push((v, v + 1));
+            }
+            if i + 1 < height {
+                edges.push((v, v + (width as u64)));
+            }
+        }
+    }
+    edges
+}
+
+fn benchmark_insert_edge(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_insert_edge");
+
+    for &size in &[100, 500, 1000, 5000] {
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), &size, |b, &size| {
+            b.iter_batched(
+                || {
+                    // Setup: create graph and wrapper with existing edges
+                    let graph = Arc::new(DynamicGraph::new());
+                    let edges = generate_path_edges(size);
+
+                    for (i, (u, v)) in edges.iter().enumerate() {
+                        graph.insert_edge(*u, *v, 1.0).unwrap();
+                    }
+
+                    let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                    for (i, (u, v)) in edges.iter().enumerate() {
+                        wrapper.insert_edge(i as u64, *u, *v);
+                    }
+
+                    // Prepare new edge to insert
+                    let new_u = 0;
+                    let new_v = size as u64 / 2;
+
+                    (graph, wrapper, new_u, new_v, size as u64)
+                },
+                |(graph, mut wrapper, u, v, edge_id)| {
+                    // Benchmark: single insert operation
+                    if graph.insert_edge(u, v, 1.0).is_ok() {
+                        wrapper.insert_edge(edge_id, u, v);
+                    }
+                    black_box(wrapper)
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn benchmark_delete_edge(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_delete_edge");
+
+    for &size in &[100, 500, 1000] {
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), &size, |b, &size| {
+            b.iter_batched(
+                || {
+                    // Setup: create graph with cycle so deletion doesn't disconnect
+                    let graph = Arc::new(DynamicGraph::new());
+                    let edges = generate_cycle_edges(size);
+
+                    for (u, v) in &edges {
+                        graph.insert_edge(*u, *v, 1.0).unwrap();
+                    }
+
+                    let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                    for (i, (u, v)) in edges.iter().enumerate() {
+                        wrapper.insert_edge(i as u64, *u, *v);
+                    }
+
+                    // Edge to delete
+                    let del_u = 0;
+                    let del_v = 1;
+
+                    (graph, wrapper, del_u, del_v)
+                },
+                |(graph, mut wrapper, u, v)| {
+                    // Benchmark: single delete operation
+                    wrapper.delete_edge(0, u, v);
+                    black_box(wrapper)
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn benchmark_query(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_query");
+
+    for &size in &[100, 500, 1000, 5000] {
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), &size, |b, &size| {
+            b.iter_batched(
+                || {
+                    // Setup: build path graph
+                    let graph = Arc::new(DynamicGraph::new());
+                    let edges = generate_path_edges(size);
+
+                    for (u, v) in &edges {
+                        graph.insert_edge(*u, *v, 1.0).unwrap();
+                    }
+
+                    let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                    for (i, (u, v)) in edges.iter().enumerate() {
+                        wrapper.insert_edge(i as u64, *u, *v);
+                    }
+
+                    wrapper
+                },
+                |mut wrapper| {
+                    // Benchmark: query operation
+                    let result = wrapper.query();
+                    black_box(result)
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn benchmark_query_after_updates(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_query_after_updates");
+
+    for &num_updates in &[10, 50, 100, 500] {
+        group.throughput(Throughput::Elements(num_updates as u64));
+        group.bench_with_input(
+            BenchmarkId::from_parameter(num_updates),
+            &num_updates,
+            |b, &num_updates| {
+                b.iter_batched(
+                    || {
+                        // Setup: build base graph
+                        let graph = Arc::new(DynamicGraph::new());
+                        let base_size = 500;
+                        let edges = generate_path_edges(base_size);
+
+                        for (u, v) in &edges {
+                            graph.insert_edge(*u, *v, 1.0).unwrap();
+                        }
+
+                        let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                        for (i, (u, v)) in edges.iter().enumerate() {
+                            wrapper.insert_edge(i as u64, *u, *v);
+                        }
+
+                        // Add buffered updates
+                        let mut rng = StdRng::seed_from_u64(42);
+                        let mut edge_id = base_size as u64;
+
+                        for _ in 0..num_updates {
+                            let u = rng.gen_range(0..base_size as u64);
+                            let v = rng.gen_range(0..base_size as u64);
+                            if u != v && graph.insert_edge(u, v, 1.0).is_ok() {
+                                wrapper.insert_edge(edge_id, u, v);
+                                edge_id += 1;
+                            }
+                        }
+
+                        wrapper
+                    },
+                    |mut wrapper| {
+                        // Benchmark: query with buffered updates
+                        let result = wrapper.query();
+                        black_box(result)
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+    }
+
+    group.finish();
+}
+
+fn benchmark_different_topologies(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_topologies");
+    let size = 500;
+
+    // Path graph
+    group.bench_function("path", |b| {
+        b.iter_batched(
+            || {
+                let graph = Arc::new(DynamicGraph::new());
+                let edges = generate_path_edges(size);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                wrapper
+            },
+            |mut wrapper| {
+                let result = wrapper.query();
+                black_box(result)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Cycle graph
+    group.bench_function("cycle", |b| {
+        b.iter_batched(
+            || {
+                let graph = Arc::new(DynamicGraph::new());
+                let edges = generate_cycle_edges(size);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                wrapper
+            },
+            |mut wrapper| {
+                let result = wrapper.query();
+                black_box(result)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Grid graph
+    let grid_size = 22; // ~484 vertices
+    group.bench_function("grid", |b| {
+        b.iter_batched(
+            || {
+                let graph = Arc::new(DynamicGraph::new());
+                let edges = generate_grid_edges(grid_size, grid_size);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                wrapper
+            },
+            |mut wrapper| {
+                let result = wrapper.query();
+                black_box(result)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Complete graph (small due to O(n^2) edges)
+    let complete_size = 30;
+    group.bench_function("complete", |b| {
+        b.iter_batched(
+            || {
+                let graph = Arc::new(DynamicGraph::new());
+                let edges = generate_complete_edges(complete_size);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                wrapper
+            },
+            |mut wrapper| {
+                let result = wrapper.query();
+                black_box(result)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    group.finish();
+}
+
+fn benchmark_mixed_workload(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_mixed_workload");
+
+    group.bench_function("realistic_workload", |b| {
+        b.iter_batched(
+            || {
+                let graph = Arc::new(DynamicGraph::new());
+                let base_size = 1000;
+                let edges = generate_random_edges(base_size, base_size * 2, 42);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                (graph, wrapper)
+            },
+            |(graph, mut wrapper)| {
+                let mut rng = StdRng::seed_from_u64(12345);
+                let mut edge_id = 2000u64;
+
+                // Simulate realistic workload:
+                // 70% queries, 20% inserts, 10% deletes
+                for _ in 0..100 {
+                    let op = rng.gen_range(0..10);
+
+                    if op < 7 {
+                        // Query
+                        let _ = black_box(wrapper.query());
+                    } else if op < 9 {
+                        // Insert
+                        let u = rng.gen_range(0..1000);
+                        let v = rng.gen_range(0..1000);
+                        if u != v && graph.insert_edge(u, v, 1.0).is_ok() {
+                            wrapper.insert_edge(edge_id, u, v);
+                            edge_id += 1;
+                        }
+                    } else {
+                        // Delete (try to delete a random edge)
+                        let u = rng.gen_range(0..1000);
+                        let v = rng.gen_range(0..1000);
+                        if graph.delete_edge(u, v).is_ok() {
+                            wrapper.delete_edge(edge_id, u, v);
+                            edge_id += 1;
+                        }
+                    }
+                }
+
+                black_box((graph, wrapper))
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    group.finish();
+}
+
+fn benchmark_lazy_instantiation(c: &mut Criterion) {
+    let mut group = c.benchmark_group("bounded_lazy_instantiation");
+
+    group.bench_function("first_query", |b| {
+        b.iter_batched(
+            || {
+                // Setup: wrapper with no instances created yet
+                let graph = Arc::new(DynamicGraph::new());
+                let edges = generate_path_edges(500);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                assert_eq!(wrapper.num_instances(), 0, "No instances before query");
+
+                wrapper
+            },
+            |mut wrapper| {
+                // Benchmark: first query triggers instantiation
+                let result = wrapper.query();
+                black_box(result)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    group.bench_function("subsequent_query", |b| {
+        b.iter_batched(
+            || {
+                // Setup: wrapper with instances already created
+                let graph = Arc::new(DynamicGraph::new());
+                let edges = generate_path_edges(500);
+
+                for (u, v) in &edges {
+                    graph.insert_edge(*u, *v, 1.0).unwrap();
+                }
+
+                let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                for (i, (u, v)) in edges.iter().enumerate() {
+                    wrapper.insert_edge(i as u64, *u, *v);
+                }
+
+                // Trigger initial instantiation
+                let _ = wrapper.query();
+                assert!(
+                    wrapper.num_instances() > 0,
+                    "Instances created after first query"
+                );
+
+                wrapper
+            },
+            |mut wrapper| {
+                // Benchmark: subsequent query (instances already exist)
+                let result = wrapper.query();
+                black_box(result)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    benchmark_insert_edge,
+    benchmark_delete_edge,
+    benchmark_query,
+    benchmark_query_after_updates,
+    benchmark_different_topologies,
+    benchmark_mixed_workload,
+    benchmark_lazy_instantiation,
+);
+
+criterion_main!(benches);

vendor/ruvector/crates/ruvector-mincut/benches/mincut_bench.rs

@@ -0,0 +1,547 @@
+//! Benchmarks for Dynamic Minimum Cut Algorithm
+//!
+//! Measures:
+//! - Insert/delete throughput at various graph sizes
+//! - Query latency
+//! - Scaling behavior (subpolynomial verification)
+//! - Comparison with static algorithms
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use rand::prelude::*;
+use ruvector_mincut::graph::DynamicGraph;
+use std::collections::HashSet;
+
+/// Generate a random graph with n vertices and m edges
+fn generate_random_graph(n: usize, m: usize, seed: u64) -> Vec<(u64, u64, f64)> {
+    let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
+    let mut edges = Vec::with_capacity(m);
+    let mut edge_set = HashSet::new();
+
+    while edges.len() < m {
+        let u = rng.gen_range(0..n as u64);
+        let v = rng.gen_range(0..n as u64);
+        if u != v {
+            let key = if u < v { (u, v) } else { (v, u) };
+            if edge_set.insert(key) {
+                edges.push((u, v, 1.0));
+            }
+        }
+    }
+
+    edges
+}
+
+/// Generate a grid graph (good test case with known min cuts)
+fn generate_grid_graph(width: usize, height: usize) -> Vec<(u64, u64, f64)> {
+    let mut edges = Vec::new();
+    for i in 0..height {
+        for j in 0..width {
+            let v = (i * width + j) as u64;
+            if j + 1 < width {
+                edges.push((v, v + 1, 1.0));
+            }
+            if i + 1 < height {
+                edges.push((v, v + width as u64, 1.0));
+            }
+        }
+    }
+    edges
+}
+
+/// Generate a complete graph with n vertices
+fn generate_complete_graph(n: usize) -> Vec<(u64, u64, f64)> {
+    let mut edges = Vec::new();
+    for i in 0..n as u64 {
+        for j in (i + 1)..n as u64 {
+            edges.push((i, j, 1.0));
+        }
+    }
+    edges
+}
+
+/// Generate a sparse graph (average degree ~4)
+fn generate_sparse_graph(n: usize, seed: u64) -> Vec<(u64, u64, f64)> {
+    let m = n * 2; // Average degree of 4
+    generate_random_graph(n, m, seed)
+}
+
+/// Generate a dense graph (edge probability ~0.3)
+fn generate_dense_graph(n: usize, seed: u64) -> Vec<(u64, u64, f64)> {
+    let m = (n * (n - 1)) / 6; // ~30% of possible edges
+    generate_random_graph(n, m, seed)
+}
+
+/// Benchmark edge insertion throughput
+fn bench_insert_edge(c: &mut Criterion) {
+    let mut group = c.benchmark_group("insert_edge");
+
+    for size in [100, 500, 1000, 5000, 10000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| {
+            b.iter_batched(
+                || {
+                    let graph = DynamicGraph::with_capacity(size, size * 3);
+                    // Pre-populate with initial edges
+                    for (u, v, w) in &edges {
+                        let _ = graph.insert_edge(*u, *v, *w);
+                    }
+                    (graph, rand::rngs::StdRng::seed_from_u64(123))
+                },
+                |(graph, mut rng)| {
+                    let u = rng.gen_range(0..size as u64);
+                    let v = rng.gen_range(0..size as u64);
+                    if u != v && !graph.has_edge(u, v) {
+                        let _ = black_box(graph.insert_edge(u, v, 1.0));
+                    }
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+    group.finish();
+}
+
+/// Benchmark edge deletion throughput
+fn bench_delete_edge(c: &mut Criterion) {
+    let mut group = c.benchmark_group("delete_edge");
+
+    for size in [100, 500, 1000, 5000, 10000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            b.iter_batched(
+                || {
+                    let graph = DynamicGraph::with_capacity(*size, size * 3);
+                    for (u, v, w) in &edges {
+                        let _ = graph.insert_edge(*u, *v, *w);
+                    }
+                    graph
+                },
+                |graph| {
+                    let edges_list = graph.edges();
+                    if !edges_list.is_empty() {
+                        let idx = rand::thread_rng().gen_range(0..edges_list.len());
+                        let edge = edges_list[idx];
+                        let _ = black_box(graph.delete_edge(edge.source, edge.target));
+                    }
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+    group.finish();
+}
+
+/// Benchmark query operations (connectivity checks)
+fn bench_query(c: &mut Criterion) {
+    let mut group = c.benchmark_group("query_connectivity");
+
+    for size in [100, 500, 1000, 5000, 10000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            let graph = DynamicGraph::with_capacity(*size, size * 3);
+            for (u, v, w) in &edges {
+                let _ = graph.insert_edge(*u, *v, *w);
+            }
+
+            b.iter(|| black_box(graph.is_connected()));
+        });
+    }
+    group.finish();
+}
+
+/// Benchmark degree queries
+fn bench_degree_query(c: &mut Criterion) {
+    let mut group = c.benchmark_group("query_degree");
+
+    for size in [100, 500, 1000, 5000, 10000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| {
+            let graph = DynamicGraph::with_capacity(size, size * 3);
+            for (u, v, w) in &edges {
+                let _ = graph.insert_edge(*u, *v, *w);
+            }
+
+            let mut rng = rand::rngs::StdRng::seed_from_u64(456);
+            b.iter(|| {
+                let v = rng.gen_range(0..size as u64);
+                black_box(graph.degree(v))
+            });
+        });
+    }
+    group.finish();
+}
+
+/// Benchmark edge existence queries
+fn bench_has_edge_query(c: &mut Criterion) {
+    let mut group = c.benchmark_group("query_has_edge");
+
+    for size in [100, 500, 1000, 5000, 10000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| {
+            let graph = DynamicGraph::with_capacity(size, size * 3);
+            for (u, v, w) in &edges {
+                let _ = graph.insert_edge(*u, *v, *w);
+            }
+
+            let mut rng = rand::rngs::StdRng::seed_from_u64(789);
+            b.iter(|| {
+                let u = rng.gen_range(0..size as u64);
+                let v = rng.gen_range(0..size as u64);
+                black_box(graph.has_edge(u, v))
+            });
+        });
+    }
+    group.finish();
+}
+
+/// Benchmark mixed workload: 50% inserts, 30% deletes, 20% queries
+fn bench_mixed_workload(c: &mut Criterion) {
+    let mut group = c.benchmark_group("mixed_workload");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        let initial_edges = generate_random_graph(*size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| {
+            b.iter_batched(
+                || {
+                    let graph = DynamicGraph::with_capacity(size, size * 3);
+                    for (u, v, w) in &initial_edges {
+                        let _ = graph.insert_edge(*u, *v, *w);
+                    }
+                    (graph, rand::rngs::StdRng::seed_from_u64(999))
+                },
+                |(graph, mut rng)| {
+                    let op = rng.gen_range(0..10);
+
+                    match op {
+                        // 50% inserts (0-4)
+                        0..=4 => {
+                            let u = rng.gen_range(0..size as u64);
+                            let v = rng.gen_range(0..size as u64);
+                            if u != v && !graph.has_edge(u, v) {
+                                let _ = graph.insert_edge(u, v, 1.0);
+                            }
+                        }
+                        // 30% deletes (5-7)
+                        5..=7 => {
+                            let edges_list = graph.edges();
+                            if !edges_list.is_empty() {
+                                let idx = rng.gen_range(0..edges_list.len());
+                                let edge = edges_list[idx];
+                                let _ = graph.delete_edge(edge.source, edge.target);
+                            }
+                        }
+                        // 20% queries (8-9)
+                        _ => {
+                            let u = rng.gen_range(0..size as u64);
+                            let _ = black_box(graph.degree(u));
+                        }
+                    }
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+    group.finish();
+}
+
+/// Benchmark scaling behavior - verify subpolynomial update time
+fn bench_scaling(c: &mut Criterion) {
+    let mut group = c.benchmark_group("scaling_analysis");
+    group.sample_size(20); // Reduce sample size for larger graphs
+
+    // Test sizes chosen to show subpolynomial scaling: n^(2/3)
+    let sizes = vec![100, 316, 1000, 3162, 10000];
+
+    for size in sizes {
+        let edges = generate_random_graph(size, size * 2, 42);
+
+        group.throughput(Throughput::Elements(1));
+        group.bench_with_input(
+            BenchmarkId::new("insert_scaling", size),
+            &size,
+            |b, &size| {
+                b.iter_batched(
+                    || {
+                        let graph = DynamicGraph::with_capacity(size, size * 3);
+                        for (u, v, w) in &edges {
+                            let _ = graph.insert_edge(*u, *v, *w);
+                        }
+                        (graph, rand::rngs::StdRng::seed_from_u64(111))
+                    },
+                    |(graph, mut rng)| {
+                        let u = rng.gen_range(0..size as u64);
+                        let v = rng.gen_range(0..size as u64);
+                        if u != v && !graph.has_edge(u, v) {
+                            let _ = black_box(graph.insert_edge(u, v, 1.0));
+                        }
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+
+        group.bench_with_input(BenchmarkId::new("delete_scaling", size), &size, |b, _| {
+            b.iter_batched(
+                || {
+                    let graph = DynamicGraph::with_capacity(size, size * 3);
+                    for (u, v, w) in &edges {
+                        let _ = graph.insert_edge(*u, *v, *w);
+                    }
+                    graph
+                },
+                |graph| {
+                    let edges_list = graph.edges();
+                    if !edges_list.is_empty() {
+                        let idx = rand::thread_rng().gen_range(0..edges_list.len());
+                        let edge = edges_list[idx];
+                        let _ = black_box(graph.delete_edge(edge.source, edge.target));
+                    }
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark different graph types
+fn bench_graph_types(c: &mut Criterion) {
+    let mut group = c.benchmark_group("graph_types");
+    let size = 1000;
+
+    // Random graph
+    let random_edges = generate_random_graph(size, size * 2, 42);
+    group.bench_function("random_insert", |b| {
+        b.iter_batched(
+            || DynamicGraph::with_capacity(size, size * 3),
+            |graph| {
+                for (u, v, w) in &random_edges {
+                    let _ = black_box(graph.insert_edge(*u, *v, *w));
+                }
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Grid graph (31x32 ≈ 1000 vertices)
+    let grid_edges = generate_grid_graph(31, 32);
+    group.bench_function("grid_insert", |b| {
+        b.iter_batched(
+            || DynamicGraph::with_capacity(size, size * 2),
+            |graph| {
+                for (u, v, w) in &grid_edges {
+                    let _ = black_box(graph.insert_edge(*u, *v, *w));
+                }
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Complete graph (45 vertices ≈ 990 edges)
+    let complete_edges = generate_complete_graph(45);
+    group.bench_function("complete_insert", |b| {
+        b.iter_batched(
+            || DynamicGraph::with_capacity(45, 1000),
+            |graph| {
+                for (u, v, w) in &complete_edges {
+                    let _ = black_box(graph.insert_edge(*u, *v, *w));
+                }
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Sparse graph
+    let sparse_edges = generate_sparse_graph(size, 42);
+    group.bench_function("sparse_insert", |b| {
+        b.iter_batched(
+            || DynamicGraph::with_capacity(size, size * 3),
+            |graph| {
+                for (u, v, w) in &sparse_edges {
+                    let _ = black_box(graph.insert_edge(*u, *v, *w));
+                }
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Dense graph
+    let dense_edges = generate_dense_graph(size, 42);
+    group.bench_function("dense_insert", |b| {
+        b.iter_batched(
+            || DynamicGraph::with_capacity(size, dense_edges.len() + 100),
+            |graph| {
+                for (u, v, w) in &dense_edges {
+                    let _ = black_box(graph.insert_edge(*u, *v, *w));
+                }
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    group.finish();
+}
+
+/// Benchmark graph statistics computation
+fn bench_stats(c: &mut Criterion) {
+    let mut group = c.benchmark_group("stats_computation");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            let graph = DynamicGraph::with_capacity(*size, size * 3);
+            for (u, v, w) in &edges {
+                let _ = graph.insert_edge(*u, *v, *w);
+            }
+
+            b.iter(|| black_box(graph.stats()));
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark connected components computation
+fn bench_connected_components(c: &mut Criterion) {
+    let mut group = c.benchmark_group("connected_components");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        // Create a graph with multiple components
+        let mut edges = Vec::new();
+        let component_size = size / 5; // 5 components
+
+        for comp in 0..5 {
+            let offset = comp * component_size;
+            for i in 0..component_size - 1 {
+                let u = (offset + i) as u64;
+                let v = (offset + i + 1) as u64;
+                edges.push((u, v, 1.0));
+            }
+        }
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            let graph = DynamicGraph::with_capacity(*size, edges.len());
+            for (u, v, w) in &edges {
+                let _ = graph.insert_edge(*u, *v, *w);
+            }
+
+            b.iter(|| black_box(graph.connected_components()));
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark neighbor iteration
+fn bench_neighbors(c: &mut Criterion) {
+    let mut group = c.benchmark_group("neighbors_iteration");
+
+    for degree in [10, 50, 100, 500, 1000].iter() {
+        // Create a star graph with one high-degree vertex
+        let mut edges = Vec::new();
+        for i in 1..=*degree {
+            edges.push((0, i as u64, 1.0));
+        }
+
+        group.bench_with_input(BenchmarkId::from_parameter(degree), degree, |b, _| {
+            let graph = DynamicGraph::with_capacity(*degree + 1, *degree);
+            for (u, v, w) in &edges {
+                let _ = graph.insert_edge(*u, *v, *w);
+            }
+
+            b.iter(|| black_box(graph.neighbors(0)));
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark batch operations
+fn bench_batch_operations(c: &mut Criterion) {
+    let mut group = c.benchmark_group("batch_operations");
+
+    for batch_size in [10, 50, 100, 500, 1000].iter() {
+        let edges = generate_random_graph(5000, *batch_size, 42);
+
+        group.throughput(Throughput::Elements(*batch_size as u64));
+        group.bench_with_input(
+            BenchmarkId::new("batch_insert", batch_size),
+            batch_size,
+            |b, _| {
+                b.iter_batched(
+                    || DynamicGraph::with_capacity(5000, *batch_size + 100),
+                    |graph| {
+                        for (u, v, w) in &edges {
+                            let _ = black_box(graph.insert_edge(*u, *v, *w));
+                        }
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+    }
+
+    group.finish();
+}
+
+/// Benchmark memory footprint (using num_edges as proxy)
+fn bench_memory_efficiency(c: &mut Criterion) {
+    let mut group = c.benchmark_group("memory_efficiency");
+
+    for size in [1000, 5000, 10000].iter() {
+        let edges = generate_random_graph(*size, size * 2, 42);
+
+        group.bench_with_input(BenchmarkId::new("graph_creation", size), size, |b, _| {
+            b.iter(|| {
+                let graph = DynamicGraph::with_capacity(*size, size * 3);
+                for (u, v, w) in &edges {
+                    let _ = graph.insert_edge(*u, *v, *w);
+                }
+                black_box(graph)
+            });
+        });
+    }
+
+    group.finish();
+}
+
+criterion_group!(
+    graph_ops,
+    bench_insert_edge,
+    bench_delete_edge,
+    bench_degree_query,
+    bench_has_edge_query,
+);
+
+criterion_group!(
+    queries,
+    bench_query,
+    bench_stats,
+    bench_connected_components,
+    bench_neighbors,
+);
+
+criterion_group!(workloads, bench_mixed_workload, bench_batch_operations,);
+
+criterion_group!(
+    scaling,
+    bench_scaling,
+    bench_graph_types,
+    bench_memory_efficiency,
+);
+
+criterion_main!(graph_ops, queries, workloads, scaling);

vendor/ruvector/crates/ruvector-mincut/benches/optimization_bench.rs

@@ -0,0 +1,368 @@
+//! Benchmark suite for j-Tree + BMSSP optimizations
+//!
+//! Measures before/after performance for each optimization:
+//! - DSpar: 5.9x target
+//! - Cache: 10x target
+//! - SIMD: 2-4x target
+//! - Pool: 50-75% memory reduction
+//! - Parallel: Near-linear scaling
+//! - WASM Batch: 10x FFI reduction
+//!
+//! Target: Combined 10x speedup
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use ruvector_mincut::graph::DynamicGraph;
+use ruvector_mincut::optimization::{
+    BatchConfig, BenchmarkSuite, CacheConfig, DegreePresparse, DistanceArray, LevelData, LevelPool,
+    LevelUpdateResult, ParallelConfig, ParallelLevelUpdater, PathDistanceCache, PoolConfig,
+    PresparseConfig, SimdDistanceOps, WasmBatchOps,
+};
+use std::collections::HashSet;
+
+/// Create test graph with specified size
+fn create_test_graph(vertices: usize, edges: usize) -> DynamicGraph {
+    let graph = DynamicGraph::new();
+
+    for i in 0..vertices {
+        graph.add_vertex(i as u64);
+    }
+
+    let mut edge_count = 0;
+    for i in 0..vertices {
+        for j in (i + 1)..vertices {
+            if edge_count >= edges {
+                break;
+            }
+            let _ = graph.insert_edge(i as u64, j as u64, 1.0);
+            edge_count += 1;
+        }
+        if edge_count >= edges {
+            break;
+        }
+    }
+
+    graph
+}
+
+/// Benchmark DSpar sparsification
+fn bench_dspar(c: &mut Criterion) {
+    let mut group = c.benchmark_group("DSpar");
+
+    for size in [100, 1000, 5000].iter() {
+        let graph = create_test_graph(*size, size * 5);
+
+        group.bench_with_input(BenchmarkId::new("baseline", size), size, |b, _| {
+            b.iter(|| {
+                let edges: Vec<_> = graph.edges().collect();
+                black_box(edges.len())
+            })
+        });
+
+        let mut dspar = DegreePresparse::with_config(PresparseConfig {
+            target_sparsity: 0.1,
+            ..Default::default()
+        });
+
+        group.bench_with_input(BenchmarkId::new("optimized", size), size, |b, _| {
+            b.iter(|| {
+                let result = dspar.presparse(&graph);
+                black_box(result.edges.len())
+            })
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark path distance cache
+fn bench_cache(c: &mut Criterion) {
+    let mut group = c.benchmark_group("PathCache");
+
+    for size in [100, 1000, 5000].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("baseline_no_cache", size),
+            size,
+            |b, &size| {
+                b.iter(|| {
+                    let mut total = 0.0;
+                    for i in 0..size {
+                        total += (i as f64 * 1.414).sqrt();
+                    }
+                    black_box(total)
+                })
+            },
+        );
+
+        let cache = PathDistanceCache::with_config(CacheConfig {
+            max_entries: *size,
+            ..Default::default()
+        });
+
+        // Pre-populate cache
+        for i in 0..*size {
+            cache.insert(i as u64, (i + 1) as u64, (i as f64).sqrt());
+        }
+
+        group.bench_with_input(
+            BenchmarkId::new("optimized_with_cache", size),
+            size,
+            |b, &size| {
+                b.iter(|| {
+                    let mut total = 0.0;
+                    for i in 0..size {
+                        if let Some(d) = cache.get(i as u64, (i + 1) as u64) {
+                            total += d;
+                        }
+                    }
+                    black_box(total)
+                })
+            },
+        );
+    }
+
+    group.finish();
+}
+
+/// Benchmark SIMD distance operations
+fn bench_simd(c: &mut Criterion) {
+    let mut group = c.benchmark_group("SIMD");
+
+    for size in [100, 1000, 10000].iter() {
+        let mut arr = DistanceArray::new(*size);
+        for i in 0..*size {
+            arr.set(i as u64, (i as f64) * 0.5 + 1.0);
+        }
+        arr.set((*size / 2) as u64, 0.1);
+
+        group.bench_with_input(BenchmarkId::new("find_min_naive", size), &arr, |b, arr| {
+            b.iter(|| {
+                let data = arr.as_slice();
+                let mut min_val = f64::INFINITY;
+                let mut min_idx = 0;
+                for (i, &d) in data.iter().enumerate() {
+                    if d < min_val {
+                        min_val = d;
+                        min_idx = i;
+                    }
+                }
+                black_box((min_val, min_idx))
+            })
+        });
+
+        group.bench_with_input(BenchmarkId::new("find_min_simd", size), &arr, |b, arr| {
+            b.iter(|| black_box(SimdDistanceOps::find_min(arr)))
+        });
+
+        let neighbors: Vec<_> = (0..(size / 10).min(100))
+            .map(|i| ((i * 10) as u64, 1.0))
+            .collect();
+
+        group.bench_with_input(
+            BenchmarkId::new("relax_batch_naive", size),
+            size,
+            |b, &size| {
+                let mut arr = DistanceArray::new(size);
+                b.iter(|| {
+                    let data = arr.as_mut_slice();
+                    for &(idx, weight) in &neighbors {
+                        let idx = idx as usize;
+                        if idx < data.len() {
+                            let new_dist = 0.0 + weight;
+                            if new_dist < data[idx] {
+                                data[idx] = new_dist;
+                            }
+                        }
+                    }
+                    black_box(())
+                })
+            },
+        );
+
+        group.bench_with_input(
+            BenchmarkId::new("relax_batch_simd", size),
+            size,
+            |b, &size| {
+                let mut arr = DistanceArray::new(size);
+                b.iter(|| black_box(SimdDistanceOps::relax_batch(&mut arr, 0.0, &neighbors)))
+            },
+        );
+    }
+
+    group.finish();
+}
+
+/// Benchmark pool allocation
+fn bench_pool(c: &mut Criterion) {
+    let mut group = c.benchmark_group("Pool");
+
+    for size in [100, 1000].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("baseline_alloc_dealloc", size),
+            size,
+            |b, &size| {
+                b.iter(|| {
+                    let mut levels = Vec::new();
+                    for i in 0..10 {
+                        levels.push(LevelData::new(i, size));
+                    }
+                    black_box(levels.len())
+                })
+            },
+        );
+
+        let pool = LevelPool::with_config(PoolConfig {
+            max_materialized_levels: 5,
+            lazy_dealloc: true,
+            ..Default::default()
+        });
+
+        group.bench_with_input(
+            BenchmarkId::new("optimized_pool", size),
+            size,
+            |b, &size| {
+                b.iter(|| {
+                    for i in 0..10 {
+                        let level = pool.allocate_level(i, size);
+                        pool.materialize(i, level);
+                    }
+                    black_box(pool.stats().materialized_levels)
+                })
+            },
+        );
+    }
+
+    group.finish();
+}
+
+/// Benchmark parallel processing
+fn bench_parallel(c: &mut Criterion) {
+    let mut group = c.benchmark_group("Parallel");
+
+    let levels: Vec<usize> = (0..100).collect();
+
+    for work_size in [10, 100, 1000].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("sequential", work_size),
+            work_size,
+            |b, &work_size| {
+                b.iter(|| {
+                    let _results: Vec<_> = levels
+                        .iter()
+                        .map(|&level| {
+                            let mut sum = 0.0;
+                            for i in 0..work_size {
+                                sum += (i as f64).sqrt();
+                            }
+                            LevelUpdateResult {
+                                level,
+                                cut_value: sum,
+                                partition: HashSet::new(),
+                                time_us: 0,
+                            }
+                        })
+                        .collect();
+                    black_box(())
+                })
+            },
+        );
+
+        let updater = ParallelLevelUpdater::with_config(ParallelConfig {
+            min_parallel_size: 10,
+            ..Default::default()
+        });
+
+        group.bench_with_input(
+            BenchmarkId::new("parallel_rayon", work_size),
+            work_size,
+            |b, &work_size| {
+                b.iter(|| {
+                    let _results = updater.process_parallel(&levels, |level| {
+                        let mut sum = 0.0;
+                        for i in 0..work_size {
+                            sum += (i as f64).sqrt();
+                        }
+                        LevelUpdateResult {
+                            level,
+                            cut_value: sum,
+                            partition: HashSet::new(),
+                            time_us: 0,
+                        }
+                    });
+                    black_box(())
+                })
+            },
+        );
+    }
+
+    group.finish();
+}
+
+/// Benchmark WASM batch operations
+fn bench_wasm_batch(c: &mut Criterion) {
+    let mut group = c.benchmark_group("WASM_Batch");
+
+    for size in [100, 1000, 5000].iter() {
+        let edges: Vec<_> = (0..*size)
+            .map(|i| (i as u64, (i + 1) as u64, 1.0))
+            .collect();
+
+        group.bench_with_input(
+            BenchmarkId::new("individual_ops", size),
+            &edges,
+            |b, edges| {
+                b.iter(|| {
+                    for edge in edges {
+                        black_box(edge);
+                    }
+                })
+            },
+        );
+
+        let mut batch = WasmBatchOps::with_config(BatchConfig {
+            max_batch_size: 1024,
+            ..Default::default()
+        });
+
+        group.bench_with_input(BenchmarkId::new("batched_ops", size), &edges, |b, edges| {
+            b.iter(|| {
+                batch.queue_insert_edges(edges.clone());
+                let results = batch.execute_batch();
+                black_box(results.len())
+            })
+        });
+    }
+
+    group.finish();
+}
+
+/// Run complete benchmark suite
+fn bench_complete_suite(c: &mut Criterion) {
+    let mut group = c.benchmark_group("Complete_Suite");
+
+    group.bench_function("full_optimization_suite", |b| {
+        b.iter(|| {
+            let mut suite = BenchmarkSuite::new()
+                .with_sizes(vec![100])
+                .with_iterations(1);
+
+            let results = suite.run_all();
+            let combined = suite.combined_speedup();
+            black_box((results.len(), combined))
+        })
+    });
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    bench_dspar,
+    bench_cache,
+    bench_simd,
+    bench_pool,
+    bench_parallel,
+    bench_wasm_batch,
+    bench_complete_suite,
+);
+
+criterion_main!(benches);

vendor/ruvector/crates/ruvector-mincut/benches/paper_algorithms_bench.rs

@@ -0,0 +1,305 @@
+//! Benchmarks for 2025 paper algorithm implementations
+//!
+//! Tests performance of:
+//! - PolylogConnectivity (arXiv:2510.08297)
+//! - ApproxMinCut (SODA 2025, arXiv:2412.15069)
+//! - CacheOptBFS
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use ruvector_mincut::connectivity::cache_opt::{BatchProcessor, CacheOptAdjacency, CacheOptBFS};
+use ruvector_mincut::{
+    ApproxMinCut, ApproxMinCutConfig, DynamicConnectivity, PolylogConnectivity, PolylogStats,
+};
+
+/// Generate a random graph with n vertices and m edges
+fn generate_graph(n: usize, m: usize, seed: u64) -> Vec<(u64, u64)> {
+    let mut edges = Vec::with_capacity(m);
+    let mut rng = seed;
+
+    for _ in 0..m {
+        // Simple LCG for deterministic "random" numbers
+        rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1);
+        let u = (rng % n as u64) as u64;
+        rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1);
+        let v = (rng % n as u64) as u64;
+        if u != v {
+            edges.push((u, v));
+        }
+    }
+    edges
+}
+
+/// Generate weighted graph edges
+fn generate_weighted_graph(n: usize, m: usize, seed: u64) -> Vec<(u64, u64, f64)> {
+    let mut edges = Vec::with_capacity(m);
+    let mut rng = seed;
+
+    for _ in 0..m {
+        rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1);
+        let u = (rng % n as u64) as u64;
+        rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1);
+        let v = (rng % n as u64) as u64;
+        rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1);
+        let w = ((rng % 100) as f64 + 1.0) / 10.0;
+        if u != v {
+            edges.push((u, v, w));
+        }
+    }
+    edges
+}
+
+// ============================================================================
+// PolylogConnectivity Benchmarks
+// ============================================================================
+
+fn bench_polylog_insert(c: &mut Criterion) {
+    let mut group = c.benchmark_group("polylog_insert");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        let edges = generate_graph(*size, size * 2, 42);
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            b.iter(|| {
+                let mut conn = PolylogConnectivity::new();
+                for &(u, v) in &edges {
+                    conn.insert_edge(u, v);
+                }
+                black_box(conn.component_count())
+            });
+        });
+    }
+    group.finish();
+}
+
+fn bench_polylog_delete(c: &mut Criterion) {
+    let mut group = c.benchmark_group("polylog_delete");
+
+    for size in [100, 500, 1000].iter() {
+        let edges = generate_graph(*size, size * 2, 42);
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            b.iter_batched(
+                || {
+                    let mut conn = PolylogConnectivity::new();
+                    for &(u, v) in &edges {
+                        conn.insert_edge(u, v);
+                    }
+                    conn
+                },
+                |mut conn| {
+                    // Delete half the edges
+                    for i in 0..edges.len() / 2 {
+                        let (u, v) = edges[i];
+                        conn.delete_edge(u, v);
+                    }
+                    black_box(conn.component_count())
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+    group.finish();
+}
+
+fn bench_polylog_query(c: &mut Criterion) {
+    let mut group = c.benchmark_group("polylog_query");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        let edges = generate_graph(*size, size * 2, 42);
+        let mut conn = PolylogConnectivity::new();
+        for &(u, v) in &edges {
+            conn.insert_edge(u, v);
+        }
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| {
+            let queries: Vec<(u64, u64)> = (0..100)
+                .map(|i| {
+                    (
+                        (i * 7) as u64 % size as u64,
+                        (i * 13 + 1) as u64 % size as u64,
+                    )
+                })
+                .collect();
+
+            b.iter(|| {
+                let mut count = 0;
+                for &(u, v) in &queries {
+                    if conn.connected(u, v) {
+                        count += 1;
+                    }
+                }
+                black_box(count)
+            });
+        });
+    }
+    group.finish();
+}
+
+// ============================================================================
+// ApproxMinCut Benchmarks
+// ============================================================================
+
+fn bench_approx_insert(c: &mut Criterion) {
+    let mut group = c.benchmark_group("approx_mincut_insert");
+
+    for size in [100, 500, 1000].iter() {
+        let edges = generate_weighted_graph(*size, size * 2, 42);
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            b.iter(|| {
+                let mut approx = ApproxMinCut::with_epsilon(0.1);
+                for &(u, v, w) in &edges {
+                    approx.insert_edge(u, v, w);
+                }
+                black_box(approx.edge_count())
+            });
+        });
+    }
+    group.finish();
+}
+
+fn bench_approx_query(c: &mut Criterion) {
+    let mut group = c.benchmark_group("approx_mincut_query");
+
+    for size in [50, 100, 200, 500].iter() {
+        let edges = generate_weighted_graph(*size, size * 2, 42);
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            b.iter_batched(
+                || {
+                    let mut approx = ApproxMinCut::with_epsilon(0.1);
+                    for &(u, v, w) in &edges {
+                        approx.insert_edge(u, v, w);
+                    }
+                    approx
+                },
+                |mut approx| black_box(approx.min_cut_value()),
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+    group.finish();
+}
+
+fn bench_approx_epsilon_comparison(c: &mut Criterion) {
+    let mut group = c.benchmark_group("approx_epsilon");
+    let size = 200;
+    let edges = generate_weighted_graph(size, size * 2, 42);
+
+    for epsilon in [0.05, 0.1, 0.2, 0.5].iter() {
+        group.bench_with_input(BenchmarkId::from_parameter(epsilon), epsilon, |b, &eps| {
+            b.iter_batched(
+                || {
+                    let mut approx = ApproxMinCut::with_epsilon(eps);
+                    for &(u, v, w) in &edges {
+                        approx.insert_edge(u, v, w);
+                    }
+                    approx
+                },
+                |mut approx| black_box(approx.min_cut_value()),
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+    group.finish();
+}
+
+// ============================================================================
+// CacheOptBFS Benchmarks
+// ============================================================================
+
+fn bench_cache_opt_bfs(c: &mut Criterion) {
+    let mut group = c.benchmark_group("cache_opt_bfs");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        let edges: Vec<(u64, u64, f64)> = generate_weighted_graph(*size, size * 3, 42);
+        let max_v = edges
+            .iter()
+            .map(|(u, v, _)| (*u).max(*v))
+            .max()
+            .unwrap_or(0);
+        let adj = CacheOptAdjacency::from_edges(&edges, max_v);
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            b.iter(|| {
+                let bfs = CacheOptBFS::new(&adj, 0);
+                black_box(bfs.run().len())
+            });
+        });
+    }
+    group.finish();
+}
+
+fn bench_batch_processor(c: &mut Criterion) {
+    let mut group = c.benchmark_group("batch_processor");
+
+    for size in [100, 500, 1000, 5000].iter() {
+        let edges: Vec<(u64, u64, f64)> = generate_weighted_graph(*size, size * 3, 42);
+        let max_v = edges
+            .iter()
+            .map(|(u, v, _)| (*u).max(*v))
+            .max()
+            .unwrap_or(0);
+        let adj = CacheOptAdjacency::from_edges(&edges, max_v);
+        let vertices: Vec<u64> = (0..=max_v).collect();
+
+        group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, _| {
+            let processor = BatchProcessor::new();
+            b.iter(|| {
+                let degrees = processor.compute_degrees(&adj, &vertices);
+                black_box(degrees.len())
+            });
+        });
+    }
+    group.finish();
+}
+
+// ============================================================================
+// Comparison: PolylogConnectivity vs DynamicConnectivity
+// ============================================================================
+
+fn bench_connectivity_comparison(c: &mut Criterion) {
+    let mut group = c.benchmark_group("connectivity_comparison");
+
+    let size = 500;
+    let edges = generate_graph(size, size * 2, 42);
+
+    // Polylog insert
+    group.bench_function("polylog_build_500", |b| {
+        b.iter(|| {
+            let mut conn = PolylogConnectivity::new();
+            for &(u, v) in &edges {
+                conn.insert_edge(u, v);
+            }
+            black_box(conn.component_count())
+        });
+    });
+
+    // Standard DynamicConnectivity insert
+    group.bench_function("dynamic_build_500", |b| {
+        b.iter(|| {
+            let mut conn = DynamicConnectivity::new();
+            for &(u, v) in &edges {
+                conn.insert_edge(u, v);
+            }
+            black_box(conn.component_count())
+        });
+    });
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    bench_polylog_insert,
+    bench_polylog_delete,
+    bench_polylog_query,
+    bench_approx_insert,
+    bench_approx_query,
+    bench_approx_epsilon_comparison,
+    bench_cache_opt_bfs,
+    bench_batch_processor,
+    bench_connectivity_comparison,
+);
+
+criterion_main!(benches);

vendor/ruvector/crates/ruvector-mincut/benches/snn_bench.rs

@@ -0,0 +1,259 @@
+//! Benchmarks for SNN-MinCut Integration
+//!
+//! Measures:
+//! - LIF neuron step performance
+//! - STDP weight update throughput
+//! - Network propagation latency
+//! - Karger-Stein mincut performance
+//! - Full cognitive engine throughput
+//! - Synchrony computation efficiency
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use ruvector_mincut::graph::DynamicGraph;
+use ruvector_mincut::snn::{
+    compute_synchrony, AttractorConfig, AttractorDynamics, CognitiveMinCutEngine, EngineConfig,
+    LIFNeuron, LayerConfig, NetworkConfig, NeuronConfig, Spike, SpikeTrain, SpikingNetwork,
+    SynapseMatrix,
+};
+
+/// Generate a random graph for benchmarking
+fn create_test_graph(n: usize) -> DynamicGraph {
+    let graph = DynamicGraph::new();
+
+    // Add vertices
+    for i in 0..n {
+        graph.add_vertex(i as u64);
+    }
+
+    // Add random edges (sparse graph ~3 edges per vertex)
+    let mut seed: u64 = 12345;
+    for i in 0..n {
+        for _ in 0..3 {
+            seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1);
+            let j = (seed % n as u64) as usize;
+            if i != j {
+                let weight = ((seed % 100) as f64) / 100.0 + 0.5;
+                let _ = graph.insert_edge(i as u64, j as u64, weight);
+            }
+        }
+    }
+
+    graph
+}
+
+/// Benchmark LIF neuron step
+fn bench_lif_neuron(c: &mut Criterion) {
+    let mut group = c.benchmark_group("lif_neuron");
+
+    for size in [100, 1000, 10000].iter() {
+        group.throughput(Throughput::Elements(*size as u64));
+
+        group.bench_with_input(BenchmarkId::new("step", size), size, |b, &size| {
+            let config = NeuronConfig::default();
+            let mut neurons: Vec<LIFNeuron> = (0..size)
+                .map(|i| LIFNeuron::with_config(i, config.clone()))
+                .collect();
+            let currents: Vec<f64> = (0..size).map(|i| (i % 10) as f64 * 0.1).collect();
+
+            b.iter(|| {
+                let mut spikes = 0;
+                for (neuron, &current) in neurons.iter_mut().zip(currents.iter()) {
+                    if neuron.step(black_box(current), 1.0, 0.0) {
+                        spikes += 1;
+                    }
+                }
+                black_box(spikes)
+            });
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark STDP weight updates
+fn bench_stdp(c: &mut Criterion) {
+    let mut group = c.benchmark_group("stdp");
+
+    for size in [100, 500, 1000].iter() {
+        let n_synapses = size * size / 10; // ~10% connectivity
+        group.throughput(Throughput::Elements(n_synapses as u64));
+
+        group.bench_with_input(BenchmarkId::new("update", size), size, |b, &size| {
+            let mut matrix = SynapseMatrix::new(size, size);
+
+            // Create sparse connections
+            let mut seed: u64 = 42;
+            for i in 0..size {
+                for _ in 0..size / 10 {
+                    seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1);
+                    let j = (seed as usize) % size;
+                    matrix.add_synapse(i, j, 0.5);
+                }
+            }
+
+            b.iter(|| {
+                // Simulate spike events
+                for i in 0..size / 10 {
+                    matrix.on_pre_spike(i, black_box(i as f64));
+                    matrix.on_post_spike(i + 1, black_box(i as f64 + 5.0));
+                }
+            });
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark spiking network propagation
+fn bench_network(c: &mut Criterion) {
+    let mut group = c.benchmark_group("spiking_network");
+
+    for &(input, hidden, output) in [(100, 50, 10), (500, 200, 50), (1000, 500, 100)].iter() {
+        let total = input + hidden + output;
+        group.throughput(Throughput::Elements(total as u64));
+
+        let name = format!("{}-{}-{}", input, hidden, output);
+        group.bench_with_input(
+            BenchmarkId::new("step", &name),
+            &(input, hidden, output),
+            |b, &(i, h, o)| {
+                let config = NetworkConfig {
+                    layers: vec![
+                        LayerConfig::new(i),
+                        LayerConfig::new(h),
+                        LayerConfig::new(o),
+                    ],
+                    ..NetworkConfig::default()
+                };
+                let mut network = SpikingNetwork::new(config);
+
+                b.iter(|| black_box(network.step()));
+            },
+        );
+    }
+
+    group.finish();
+}
+
+/// Benchmark attractor dynamics (includes Karger-Stein)
+fn bench_attractor(c: &mut Criterion) {
+    let mut group = c.benchmark_group("attractor_dynamics");
+    group.sample_size(50); // Fewer samples for slower benchmarks
+
+    for size in [50, 100, 200].iter() {
+        group.throughput(Throughput::Elements(*size as u64));
+
+        group.bench_with_input(BenchmarkId::new("step", size), size, |b, &size| {
+            let graph = create_test_graph(size);
+            let config = AttractorConfig::default();
+            let mut attractor = AttractorDynamics::new(graph, config);
+
+            b.iter(|| black_box(attractor.step()));
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark synchrony computation
+fn bench_synchrony(c: &mut Criterion) {
+    let mut group = c.benchmark_group("synchrony");
+
+    for size in [100, 1000, 10000].iter() {
+        group.throughput(Throughput::Elements(*size as u64));
+
+        group.bench_with_input(BenchmarkId::new("compute", size), size, |b, &size| {
+            // Generate random spikes
+            let mut seed: u64 = 999;
+            let spikes: Vec<Spike> = (0..size)
+                .map(|i| {
+                    seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1);
+                    Spike {
+                        neuron_id: (seed as usize) % 100,
+                        time: (i as f64) + ((seed % 100) as f64) / 100.0,
+                    }
+                })
+                .collect();
+
+            b.iter(|| black_box(compute_synchrony(&spikes, 10.0)));
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark full cognitive engine
+fn bench_cognitive_engine(c: &mut Criterion) {
+    let mut group = c.benchmark_group("cognitive_engine");
+    group.sample_size(30); // Fewer samples for complex benchmark
+
+    for size in [50, 100].iter() {
+        group.throughput(Throughput::Elements(*size as u64));
+
+        group.bench_with_input(BenchmarkId::new("step", size), size, |b, &size| {
+            let graph = create_test_graph(size);
+            let config = EngineConfig::default();
+            let mut engine = CognitiveMinCutEngine::new(graph, config);
+
+            b.iter(|| black_box(engine.step()));
+        });
+
+        group.bench_with_input(BenchmarkId::new("run_10", size), size, |b, &size| {
+            let graph = create_test_graph(size);
+            let config = EngineConfig::default();
+            let mut engine = CognitiveMinCutEngine::new(graph, config);
+
+            b.iter(|| black_box(engine.run(10)));
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark spike train operations
+fn bench_spike_train(c: &mut Criterion) {
+    let mut group = c.benchmark_group("spike_train");
+
+    for size in [100, 1000, 5000].iter() {
+        group.throughput(Throughput::Elements(*size as u64));
+
+        group.bench_with_input(BenchmarkId::new("to_pattern", size), size, |b, &size| {
+            let mut train = SpikeTrain::new(0);
+            for i in 0..size {
+                train.record_spike(i as f64 * 0.5);
+            }
+
+            b.iter(|| black_box(train.to_pattern(0.0, 1.0, 100)));
+        });
+
+        group.bench_with_input(
+            BenchmarkId::new("cross_correlation", size),
+            size,
+            |b, &size| {
+                let mut train1 = SpikeTrain::new(0);
+                let mut train2 = SpikeTrain::new(1);
+                for i in 0..size {
+                    train1.record_spike(i as f64 * 0.5);
+                    train2.record_spike(i as f64 * 0.5 + 2.0);
+                }
+
+                b.iter(|| black_box(train1.cross_correlation(&train2, 50.0, 1.0)));
+            },
+        );
+    }
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    bench_lif_neuron,
+    bench_stdp,
+    bench_network,
+    bench_attractor,
+    bench_synchrony,
+    bench_cognitive_engine,
+    bench_spike_train,
+);
+
+criterion_main!(benches);

vendor/ruvector/crates/ruvector-mincut/benches/sota_bench.rs

@@ -0,0 +1,545 @@
+//! SOTA (State-of-the-Art) Benchmarks
+//!
+//! Benchmarks for the advanced optimizations implemented from the December 2025 paper:
+//! - Cut size scaling behavior
+//! - Graph density impact
+//! - Batch operation efficiency
+//! - Memory pool performance
+//! - Lazy witness benefits
+//! - Replacement edge lookup
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use rand::rngs::StdRng;
+use rand::{Rng, SeedableRng};
+use ruvector_mincut::algorithm::ReplacementEdgeIndex;
+use ruvector_mincut::instance::witness::LazyWitness;
+use ruvector_mincut::pool::BfsPool;
+use ruvector_mincut::prelude::*;
+use ruvector_mincut::wrapper::MinCutWrapper;
+use std::collections::{HashMap, HashSet};
+use std::sync::Arc;
+
+// ============================================================================
+// Graph Generators
+// ============================================================================
+
+/// Generate a graph with a specific minimum cut value
+/// Creates two cliques connected by k edges (min cut = k)
+fn generate_known_mincut(n_per_side: usize, mincut_value: usize, seed: u64) -> Vec<(u64, u64)> {
+    let mut edges = Vec::new();
+    let mut rng = StdRng::seed_from_u64(seed);
+
+    // First clique: vertices 0 to n_per_side-1
+    for i in 0..n_per_side as u64 {
+        for j in (i + 1)..n_per_side as u64 {
+            edges.push((i, j));
+        }
+    }
+
+    // Second clique: vertices n_per_side to 2*n_per_side-1
+    let offset = n_per_side as u64;
+    for i in 0..n_per_side as u64 {
+        for j in (i + 1)..n_per_side as u64 {
+            edges.push((offset + i, offset + j));
+        }
+    }
+
+    // Connect with exactly mincut_value edges
+    for k in 0..mincut_value {
+        let u = rng.gen_range(0..n_per_side as u64);
+        let v = offset + rng.gen_range(0..n_per_side as u64);
+        edges.push((u, v));
+    }
+
+    edges
+}
+
+/// Generate graph with specified density (0.0 = sparse, 1.0 = complete)
+fn generate_density_graph(n: usize, density: f64, seed: u64) -> Vec<(u64, u64)> {
+    let mut rng = StdRng::seed_from_u64(seed);
+    let max_edges = n * (n - 1) / 2;
+    let target_edges = ((max_edges as f64) * density) as usize;
+
+    let mut edges = Vec::with_capacity(target_edges);
+    let mut seen = HashSet::new();
+
+    while edges.len() < target_edges {
+        let u = rng.gen_range(0..n as u64);
+        let v = rng.gen_range(0..n as u64);
+        if u != v {
+            let key = if u < v { (u, v) } else { (v, u) };
+            if seen.insert(key) {
+                edges.push((u, v));
+            }
+        }
+    }
+
+    edges
+}
+
+/// Generate path graph
+fn generate_path(n: usize) -> Vec<(u64, u64)> {
+    (0..n as u64 - 1).map(|i| (i, i + 1)).collect()
+}
+
+// ============================================================================
+// Cut Size Scaling Benchmarks
+// ============================================================================
+
+/// Benchmark how performance scales with minimum cut value
+fn bench_cut_size_scaling(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_cut_size_scaling");
+    group.sample_size(50);
+
+    let n_per_side = 50; // 100 vertices total
+
+    // Test different min-cut values: 1, 2, 4, 8, 16, 32
+    for mincut in [1, 2, 4, 8, 16, 32] {
+        group.bench_with_input(
+            BenchmarkId::new("query_mincut", mincut),
+            &mincut,
+            |b, &mincut| {
+                b.iter_batched(
+                    || {
+                        let graph = Arc::new(DynamicGraph::new());
+                        let edges = generate_known_mincut(n_per_side, mincut, 42);
+
+                        for (u, v) in &edges {
+                            let _ = graph.insert_edge(*u, *v, 1.0);
+                        }
+
+                        let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                        for (i, (u, v)) in edges.iter().enumerate() {
+                            wrapper.insert_edge(i as u64, *u, *v);
+                        }
+
+                        wrapper
+                    },
+                    |mut wrapper| {
+                        let result = wrapper.query();
+                        black_box(result)
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Density Impact Benchmarks
+// ============================================================================
+
+/// Benchmark how graph density affects performance
+fn bench_density_impact(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_density_impact");
+    group.sample_size(30);
+
+    let n = 200;
+
+    // Test different densities: 0.01, 0.05, 0.1, 0.2, 0.3
+    for density_pct in [1, 5, 10, 20, 30] {
+        let density = density_pct as f64 / 100.0;
+
+        group.bench_with_input(
+            BenchmarkId::new("query_density", format!("{}pct", density_pct)),
+            &density,
+            |b, &density| {
+                b.iter_batched(
+                    || {
+                        let graph = Arc::new(DynamicGraph::new());
+                        let edges = generate_density_graph(n, density, 42);
+
+                        for (u, v) in &edges {
+                            let _ = graph.insert_edge(*u, *v, 1.0);
+                        }
+
+                        let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                        for (i, (u, v)) in edges.iter().enumerate() {
+                            wrapper.insert_edge(i as u64, *u, *v);
+                        }
+
+                        wrapper
+                    },
+                    |mut wrapper| {
+                        let result = wrapper.query();
+                        black_box(result)
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Batch Operation Benchmarks
+// ============================================================================
+
+/// Benchmark batch insert vs sequential insert
+fn bench_batch_vs_sequential(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_batch_operations");
+
+    for batch_size in [10, 50, 100, 500] {
+        // Sequential inserts
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(
+            BenchmarkId::new("sequential_insert", batch_size),
+            &batch_size,
+            |b, &batch_size| {
+                b.iter_batched(
+                    || {
+                        let graph = Arc::new(DynamicGraph::new());
+                        // Base graph
+                        let base_edges = generate_path(500);
+                        for (u, v) in &base_edges {
+                            let _ = graph.insert_edge(*u, *v, 1.0);
+                        }
+
+                        let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                        for (i, (u, v)) in base_edges.iter().enumerate() {
+                            wrapper.insert_edge(i as u64, *u, *v);
+                        }
+
+                        // New edges to insert
+                        let mut rng = StdRng::seed_from_u64(123);
+                        let new_edges: Vec<_> = (0..batch_size)
+                            .filter_map(|i| {
+                                let u = rng.gen_range(0..500);
+                                let v = rng.gen_range(0..500);
+                                if u != v && graph.insert_edge(u, v, 1.0).is_ok() {
+                                    Some((1000 + i as u64, u, v))
+                                } else {
+                                    None
+                                }
+                            })
+                            .collect();
+
+                        (wrapper, new_edges)
+                    },
+                    |(mut wrapper, edges)| {
+                        for (id, u, v) in edges {
+                            wrapper.insert_edge(id, u, v);
+                        }
+                        black_box(wrapper)
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+
+        // Batch inserts
+        group.bench_with_input(
+            BenchmarkId::new("batch_insert", batch_size),
+            &batch_size,
+            |b, &batch_size| {
+                b.iter_batched(
+                    || {
+                        let graph = Arc::new(DynamicGraph::new());
+                        let base_edges = generate_path(500);
+                        for (u, v) in &base_edges {
+                            let _ = graph.insert_edge(*u, *v, 1.0);
+                        }
+
+                        let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
+                        for (i, (u, v)) in base_edges.iter().enumerate() {
+                            wrapper.insert_edge(i as u64, *u, *v);
+                        }
+
+                        let mut rng = StdRng::seed_from_u64(123);
+                        let new_edges: Vec<_> = (0..batch_size)
+                            .filter_map(|i| {
+                                let u = rng.gen_range(0..500);
+                                let v = rng.gen_range(0..500);
+                                if u != v && graph.insert_edge(u, v, 1.0).is_ok() {
+                                    Some((1000 + i as u64, u, v))
+                                } else {
+                                    None
+                                }
+                            })
+                            .collect();
+
+                        (wrapper, new_edges)
+                    },
+                    |(mut wrapper, edges)| {
+                        wrapper.batch_insert_edges(&edges);
+                        black_box(wrapper)
+                    },
+                    criterion::BatchSize::SmallInput,
+                );
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Memory Pool Benchmarks
+// ============================================================================
+
+/// Benchmark BFS with and without pool
+fn bench_memory_pool(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_memory_pool");
+
+    for size in [100, 500, 1000, 5000] {
+        // Without pool - allocate fresh each time
+        group.bench_with_input(BenchmarkId::new("bfs_no_pool", size), &size, |b, &size| {
+            b.iter(|| {
+                let mut queue = std::collections::VecDeque::with_capacity(size);
+                let mut visited = HashSet::with_capacity(size);
+                let mut results = Vec::with_capacity(size);
+
+                // Simulate BFS work
+                queue.push_back(0u64);
+                visited.insert(0);
+                while let Some(v) = queue.pop_front() {
+                    results.push(v);
+                    if results.len() >= size {
+                        break;
+                    }
+                    // Simulate adding neighbors
+                    for next in v + 1..v + 4 {
+                        if visited.insert(next) {
+                            queue.push_back(next);
+                        }
+                    }
+                }
+
+                black_box(results)
+            });
+        });
+
+        // With pool - reuse allocations
+        group.bench_with_input(
+            BenchmarkId::new("bfs_with_pool", size),
+            &size,
+            |b, &size| {
+                b.iter(|| {
+                    let mut res = BfsPool::acquire(size);
+
+                    // Simulate BFS work
+                    res.queue.push_back(0);
+                    res.visited.insert(0);
+                    while let Some(v) = res.queue.pop_front() {
+                        res.results.push(v);
+                        if res.results.len() >= size {
+                            break;
+                        }
+                        for next in v + 1..v + 4 {
+                            if res.visited.insert(next) {
+                                res.queue.push_back(next);
+                            }
+                        }
+                    }
+
+                    black_box(res.results.len())
+                    // Resources returned on drop
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Lazy Witness Benchmarks
+// ============================================================================
+
+/// Benchmark lazy vs eager witness materialization
+fn bench_lazy_witness(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_lazy_witness");
+
+    // Build adjacency for materialization
+    let adjacency = |v: VertexId| -> Vec<VertexId> {
+        // Simple linear graph for benchmarking
+        if v == 0 {
+            vec![1]
+        } else if v < 999 {
+            vec![v - 1, v + 1]
+        } else {
+            vec![v - 1]
+        }
+    };
+
+    // Benchmark storing witnesses without materialization
+    group.bench_function("lazy_store_100", |b| {
+        b.iter(|| {
+            let witnesses: Vec<_> = (0..100)
+                .map(|i| LazyWitness::new(i as u64, 10, i as u64))
+                .collect();
+            black_box(witnesses)
+        });
+    });
+
+    // Benchmark materializing just the best witness
+    group.bench_function("lazy_materialize_best", |b| {
+        b.iter_batched(
+            || {
+                // Create 100 lazy witnesses
+                (0..100)
+                    .map(|i| LazyWitness::new(i as u64, 10, i as u64))
+                    .collect::<Vec<_>>()
+            },
+            |witnesses| {
+                // Find and materialize only the best (smallest boundary)
+                let best = witnesses.iter().min_by_key(|w| w.boundary_size()).unwrap();
+                let handle = best.materialize(&adjacency);
+                black_box(handle)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    // Benchmark materializing all witnesses
+    group.bench_function("eager_materialize_all", |b| {
+        b.iter_batched(
+            || {
+                (0..100)
+                    .map(|i| LazyWitness::new(i as u64, 10, i as u64))
+                    .collect::<Vec<_>>()
+            },
+            |witnesses| {
+                let handles: Vec<_> = witnesses
+                    .iter()
+                    .map(|w| w.materialize(&adjacency))
+                    .collect();
+                black_box(handles)
+            },
+            criterion::BatchSize::SmallInput,
+        );
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Replacement Edge Benchmarks
+// ============================================================================
+
+/// Benchmark replacement edge lookup
+fn bench_replacement_edge(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_replacement_edge");
+
+    for n in [100, 500, 1000, 5000] {
+        // Build tree adjacency
+        let mut tree_adj: HashMap<VertexId, HashSet<VertexId>> = HashMap::new();
+        for i in 0..n as u64 - 1 {
+            tree_adj.entry(i).or_default().insert(i + 1);
+            tree_adj.entry(i + 1).or_default().insert(i);
+        }
+
+        // Add non-tree edges
+        let mut rng = StdRng::seed_from_u64(42);
+        let mut idx = ReplacementEdgeIndex::new(n);
+
+        // Add tree edges
+        for i in 0..n as u64 - 1 {
+            idx.add_tree_edge(i, i + 1);
+        }
+
+        // Add non-tree edges (skip connections)
+        for _ in 0..n / 5 {
+            let u = rng.gen_range(0..n as u64);
+            let v = rng.gen_range(0..n as u64);
+            if u != v && (u as i64 - v as i64).abs() > 1 {
+                idx.add_non_tree_edge(u, v);
+            }
+        }
+
+        group.bench_with_input(BenchmarkId::new("find_replacement", n), &n, |b, _| {
+            b.iter_batched(
+                || idx.clone(),
+                |mut idx| {
+                    // Find replacement for middle edge
+                    let u = (n / 2) as u64;
+                    let v = u + 1;
+                    let result = idx.find_replacement(u, v, &tree_adj);
+                    black_box(result)
+                },
+                criterion::BatchSize::SmallInput,
+            );
+        });
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Binary Search Instance Lookup Benchmarks
+// ============================================================================
+
+/// Benchmark binary search vs linear instance lookup
+fn bench_instance_lookup(c: &mut Criterion) {
+    let mut group = c.benchmark_group("sota_instance_lookup");
+
+    for num_instances in [10, 50, 100, 500] {
+        // Simulate instance bounds
+        let instances: Vec<(u64, u64)> = (0..num_instances)
+            .map(|i| {
+                let min = ((1.2f64).powi(i as i32)).floor() as u64;
+                let max = ((1.2f64).powi((i + 1) as i32)).floor() as u64;
+                (min.max(1), max.max(1))
+            })
+            .collect();
+
+        // Linear search
+        group.bench_with_input(
+            BenchmarkId::new("linear_search", num_instances),
+            &num_instances,
+            |b, _| {
+                b.iter(|| {
+                    let target = 50u64;
+                    let found = instances
+                        .iter()
+                        .position(|(min, max)| target >= *min && target <= *max);
+                    black_box(found)
+                });
+            },
+        );
+
+        // Binary search
+        group.bench_with_input(
+            BenchmarkId::new("binary_search", num_instances),
+            &num_instances,
+            |b, _| {
+                b.iter(|| {
+                    let target = 50u64;
+                    let found = instances.binary_search_by(|(min, max)| {
+                        if target < *min {
+                            std::cmp::Ordering::Greater
+                        } else if target > *max {
+                            std::cmp::Ordering::Less
+                        } else {
+                            std::cmp::Ordering::Equal
+                        }
+                    });
+                    black_box(found)
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Criterion Groups
+// ============================================================================
+
+criterion_group!(cut_scaling, bench_cut_size_scaling,);
+
+criterion_group!(density, bench_density_impact,);
+
+criterion_group!(batch_ops, bench_batch_vs_sequential,);
+
+criterion_group!(memory, bench_memory_pool, bench_lazy_witness,);
+
+criterion_group!(lookup, bench_replacement_edge, bench_instance_lookup,);
+
+criterion_main!(cut_scaling, density, batch_ops, memory, lookup);